Tutorial: State-of-the-Art Flow Field Analysis and Visualization

Texture-Based Flow Visualization Daniel Weiskopf

IEEE VIS 2013 | Atlanta | 2013-10-13 Overview

. Background . Line integral convolution and texture advection . Hierarchical line integration . Effective rendering and feature extraction . Outlook and conclusion

Flow Visualization Approaches

. Direct approaches . Glyphs, arrows . Color coding . Sparse, line-like representations . Dense, line-like representations . Characteristic lines . Streamlines . Pathlines . Streaklines

[courtesy of BMW Group and Martin Schulz] Visualization Approaches

Arrows Sparse (topology) Dense (texture-based) [Source: G. Scheuermann]

[Weiskopf, Erlebacher 2005] Visualization Approaches

Arrows Sparse (topology) Dense (texture-based) [Source: G. Scheuermann]

see later: Vector Field Topology in Flow Analysis and Visualization

[Weiskopf, Erlebacher 2005] Visualization Approaches

Arrows Sparse (topology) Dense (texture-based) [Source: G. Scheuermann]

[Weiskopf, Erlebacher 2005] Why Texture-Based Flow Visualization?

. Dense sampling . Better coverage of information . (Partially) solved problem of seeding . Flexibility in visual representation . Good controllability of visual style . From line-like (crisp) all the way to fuzzy Dense Vector Field Visualization

. Characteristic lines . Challenges . Visualization speed . Quality . Effectiveness

. Interactive GPU methods . Hierarchical computation . Facilitate good visual perception . Combine with feature extraction

[Weiskopf et al. 2003] Ingredients of Vector Field Visualization

Advection (Transport mechanism)

Rendering VIS

Properties (What is advected? Mapping to primitives) Line Integral Convolution (LIC)

Convolution

I(x(t  s))k(s)ds Noise I Vector field Particle tracing

Kernel k(s)

-L L Result Lagrangian Particle Tracing

. Velocity vector v . Trace massless particles dr  v(r,t) . Equation of motion: dt

. Explicit integration . Step-by-step . Possible in GPU fragment shaders

see later: Foundations of Data-Parallel Particle Advection Convolution

. Discretization of the convolution integral . Integration simultaneously with particle tracing . Accumulate noise values Mapping to GPU: Parallel Computation

Convolution

Fragment shader

Noise texture Data set in texture

Kernel in 1D texture

-L L Render to texture Incremental LIC: Texture Advection [Van Wijk 2002]

. Injection of new material advected injection . IBFV idea texture texture . Injection texture compositing . Alpha blending advection step . Exponential filter kernel (steady case) . Streaklets (unsteady)

[generated by Van Wijk’s demo program] Golf of Mexico

Data set: 352 x 320, 183 time steps [Data courtesy of O‘Brien, FSU] [Weiskopf et al. 2005] 2D and 3D LIC

. 2D case: . Only 2D textures (vector field, noise, result) . Rendering is trivial: just 2D image . 3D case: . 3D textures for vector field and noise . Slice-by-slice output . Volume rendering . 3D alternative: . On-the-fly computation [Falk, Weiskopf 2008] . Integrated within GPU ray casting . Lazy evaluation: output sensitivity

3D LIC On-the-Fly

[Falk, Weiskopf 2008] 3D LIC On-the-Fly

[Falk, Weiskopf 2008] Hierarchical Line Integration [Hlawatsch et al. 2011]

. Coordinate maps 푛 휑푖 : 퐷 → 퐷 , 퐷 ⊆ ℝ

푥 : start point of traj. 휑2: 휑푖(푥) : end point of traj. 휑1: : nodes 푖 : hierarchy level 휑 : 0

. 휑0 obtained, e.g., by integration . 휑>0 constructed by “concatenation” . All levels have same resolution (no pyramid) . Overwrite (store only highest level)

• general case: end points not at nodes  interpolation Hierarchical Line Integration: Procedure

traditional approach hierarchical approach (n integration steps) (h levels)

integration of initial trajectories

 휑0

O(n) one catenation (s = 2) for next level

 휑+1

O(h) = O(log n) Hierarchical Convolution

. Perform LIC operations inside hierarchical scheme . Combine intermediate quantities from integration . Convolution of Gaussian with Gaussian is Gaussian straightforward

hierarchical

[Hlawatsch et al. 2011] Performance of Hierarchical Integration

[Hlawatsch et al. 2011] Time-Dependent Hierarchical Integration

. level 0: from data set (by integration, blue)

. green: required for result at time t1 (at level 3) . bold outlines: blocks kept in memory (overwrite) . hatched: next time blocks

. integration range  number of blocks in memory . scheme pays off for time series, i.e., dense trajectory seeding in time

. no temporal interpolation needed

[Hlawatsch et al. 2011] 2.5D LIC

. Adapted to flow on boundaries . Image-space methods [Van Wijk 2003], [Laramee et al. 2003], [Weiskopf, Ertl 2004b]

Object space

Projection Particle trace

Image plane

Eye [Weiskopf, Ertl 2004b] 2.5D LIC

[Weiskopf, Ertl 2004b]

On Pathsurfaces

[Image: Schafhitzel et al. 2007] al.et Schafhitzel [Image:

[Schafhitzel et al. 2007]

On Pathsurfaces

[Image: Schafhitzel et al. 2007] al.et Schafhitzel [Image:

[Schafhitzel et al. 2007] Effective Visual Representation

. Perceptual issues for 3D LIC and texture advection . Spatial perception: Orientation, depth . Clutter . Occlusion Improved Rendering

. Improve spatial perception [Interrante, Grosch 1997] . Illumination . Halos . Depth cues . Line continuity

[Image courtesy of Victoria Interrante]

[Courtesy of Interrante, image reprinted from Weiskopf, Erlebacher 2005] Spatial Perception

. Technical solution: Real-time volumetric illumination [Weiskopf et al. 2007], [Falk, Weiskopf 2008] . On-the-fly computation of gradients . Various illumination models (Phong, cool/warm, halos) . Tangent-based illumination Spatial Perception

. No illumination

[Weiskopf et al. 2007] Spatial Perception

. Phong illumination

[Weiskopf et al. 2007] Spatial Perception

. Cool/warm

[Weiskopf et al. 2007] Codimension-2 Illumination

. Illuminated streamlines: no gradients computed

[Falk, Weiskopf 2008] Codimension-2 Illumination

. Alternative codimension-2 illumination model (Mallo)

[Falk, Weiskopf 2008] Codimension-2 Illumination

. Comparison: without illumination

[Falk, Weiskopf 2008] Codimension-2 Illumination

. Comparison: gradient-based illumination

[Falk, Weiskopf 2008] Clutter and Occlusion

[Weiskopf, Ertl 2004a] Clutter and Occlusion

[Weiskopf, Ertl 2004a] Clutter and Occlusion

[Weiskopf, Ertl 2004a] Different Noise Models: “Seeding”

Dense (white noise) Sparse noise

[Falk, Weiskopf 2008] Different Noise Models: “Seeding”

Dense (white noise) Sparse noise see from before: Streamlines in 3D: Techniques beyond Seed Placement [Falk, Weiskopf 2008] Flow Feature Extraction

. 3D interest function . Domain knowledge . Interactive exploration

Vortex extraction

with 2

[Weiskopf et al. 2007] Flow Feature Extraction

. 2.5D visualization on 2 isosurfaces

[Schafhitzel et al. 2006] Flow Feature Extraction

. 3D LIC with 2 feature enhancement

[Falk, Weiskopf 2008] Clipping and Semi-Transparency

[Rezk-Salama et al. 1999]

Outlook: Topics Not Covered Here

. Physically oriented dye advection . Advection and diffusion [Karch et al. 2012] . Numerical quality of dye and texture advection

. Level-set and particle level-sets 2012] al. et [Karch [Weiskopf 2004b], [Cuntz et al. 2008] . WENO schemes [Karch et al. 2012] . Higher-order and BFECC advection [Netzel et al. 2012] . Quality of filtering . Frequency analysis: low-pass filter

characteristics [Netzel et al. 2012] , [Weiskopf 2009] [Netzel et al. 2012] al. et [Netzel Outlook: Topics Not Covered Here

. Relationship to dense geometric curves

and hybrid techniques [Verma et al. 1999], [Weiskopf et al. 2005]

] . Control of rendering styles . Chameleon system [Li et al. 2003]

. Non-uniform grids and higher-order reconstruction et 2003 al. [Li . Perceptual graphics . Texture, color, motion perception [Bachthaler, Weiskopf 2008], [Weiskopf 2004a]

2004a] 2004a]

Weiskopf [ Conclusion

. Texture-based vector field visualization . Flexible, widely applicable . Fast . Components . Transport mechanism . Visual representation and rendering . Visualization quality Further Material

www.vis.uni-stuttgart.de/texflowvis References

[Bachthaler, Weiskopf 2008] S. Bachthaler, D. Weiskopf: Animation of orthogonal texture patterns for vector field visualization. IEEE Transactions on Visualization and Computer Graphics 14(4), 741-755, 2008. [Cuntz et al. 2008] N. Cuntz, A. Kolb, R. Strzodka, D. Weiskopf. Particle level set advection for the interactive visualization of unsteady 3D flow. Computer Graphics Forum 27(3), 719-726, 2008. [Falk, Weiskopf 2008] M. Falk, D. Weiskopf. Output-sensitive 3D line integral convolution. IEEE Transactions on Visualization and Computer Graphics 14(4), 820-834, 2008. [Hlawatsch et al. 2011] M. Hlawatsch, F. Sadlo, D. Weiskopf. Hierarchical line integration. IEEE Transactions on Visualization and Computer Graphics 17(8), 1148-1163, 2011. [Interrante, Grosch 1997] V. Interrante, C. Grosch. Strategies for effectively visualizing 3D flow with volume LIC. Proc. IEEE Visualization '97, 421-424, 1997. [Jobard et al. 2002] B. Jobard, G. Erlebacher, M. Y. Hussaini. Lagrangian-Eulerian advection of noise and dye textures for unsteady flow visualization. IEEE Transactions on Visualization and Computer Graphics 8(3), 211-222, 2002. [Karch et al. 2012] G. Karch, F. Sadlo, D. Weiskopf, C.-D. Munz, T. Ertl: Visualization of advection-diffusion in unsteady fluid flow. Computer Graphics Forum 3, 1105-1114, 2012. [Laramee et al. 2003] R. S. Laramee, B. Jobard, H. Hauser. Image space based visualization of unsteady flow on surfaces. In Proc. IEEE Visualization ’03, 131-138, 2003. References

[Li et al. 2003] G.-S. Li, U.D. Bordoloi, H.-W. Shen. Chameleon: An interactive texture-based rendering framework for visualizing three- dimensional vector fields. In Proc. IEEE Visualization '03, 241-248, 2003. [Netzel et al. 2012] R. Netzel, M. Ament, M. Burch, D. Weiskopf. Spectral analysis of higher-order and BFECC texture advection. Proc. VMV, 87-94, 2012. [Rezk-Salama et al. 1999] C. Rezk-Salama, P. Hastreiter, C. Teitzel, T. Ertl. Interactive exploration of volume line integral convolution based on 3D-texture mapping. In Proc. IEEE Visualization '99, 233-240, 1999. [Schafhitzel et al. 2006] T. Schafhitzel, D. Weiskopf, T. Ertl. Interactive investigation and visualization of 3D vortex structures. Proc. International Symposium on Flow Visualization (ISFV), 2006. [Schafhitzel et al. 2007] T. Schafhitzel, E. Tejada, D. Weiskopf, T. Ertl. Point-based stream surfaces and path surfaces. Proc. Graphics Interface, 289-296, 2007. [Van Wijk 2002] J. van Wijk. Image based flow visualization. ACM Transactions on Graphics 21 (3), 745-754, 2002. [Van Wijk 2003] J. J. van Wijk. Image based flow visualization for curved surfaces. In Proc. IEEE Visualization ’03, 123-130, 2003. [Verma et al. 1999] V. Verma, D. T. Kao, A. Pang. PLIC: Briding the gap between streamlines and LIC. Proc. IEEE Visualization ‘99, 341-348, 1999. [Weiskopf et al. 2003] D. Weiskopf, G. Erlebacher, T. Ertl. A Texture-based framework for spacetime-coherent visualization of time- dependent vector fields. Proc. IEEE Visualization ‘03, 107-114, 2003.

References

[Weiskopf 2004a] D. Weiskopf. On the role of color in the perception of motion in animated visualizations. Proc. IEEE Visualization ’04, 305-312, 2004. [Weiskopf 2004b] D. Weiskopf. Dye advection without the blur: A level-set approach for texture-based visualization of unsteady flow. Computer Graphics Forum 23(3), 479-488, 2004. [Weiskopf, Ertl 2004a] D. Weiskopf, T. Ertl. GPU-based 3D texture advection for the visualization of unsteady flow fields. Proc. WSCG 2004 Short Papers, 259-266, 2004. [Weiskopf, Ertl 2004b] D. Weiskopf, T. Ertl. A hybrid physical/device-space approach for spatio-temporally coherent interactive texture advection on curved surfaces. Proc. Graphics Interface 2004, 263-270, 2004 [Weiskopf, Erlebacher 2005] D. Weiskopf, G. Erlebacher. Overview of flow visualization. In C. D. Hansen, C. R. Johnson (eds.): The Visualization Handbook, Elsevier, Amsterdam, 261-278, 2005. [Weiskopf et al. 2005] D. Weiskopf, F. Schramm, G. Erlebacher, T. Ertl. Particle and texture based spatiotemporal visualization of time- dependent vector fields. Proc. IEEE Visualization ‘05, 639-646, 2005. [Weiskopf et al. 2007] D. Weiskopf, T. Schafhitzel, T. Ertl. Texture-based visualization of unsteady 3D flow by real-time advection and volumetric illumination, IEEE Transactions on Visualization and Computer Graphics 13(3), 569-582, 2007. [Weiskopf 2009] D. Weiskopf. Iterative twofold line integral convolution for texture-based vector field visualization. In T. Möller, B. Hamann, R. Russell (Eds.), Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration, Springer, 191-211, 2009.